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Report Contents Report#:EIA/DOE-0607(99)
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[1] For example, see A.B. Jaffe and R.N. Stavins, “The Energy Paradox and the Diffusion of Conservation Technology,” Resource and Energy Economics, Vol. 16 (1994), pp. 91-122. [2] See Z. Griliches, “Hybrid Corn: An Exploration in the Economics of Technological Change,” Econometrica, Vol. 25 (1957), pp. 501-522; S.W. Davies, “Inter-firm Diffusion of Process Innovations,” European Economic Review, Vol. 12 (1979), pp. 299-317; S. Oster, “The Diffusion of Innovation Among Steel Firms: The Basic Oxygen Furnace.” Bell Journal of Economics, Vol. 13 (1982), pp. 45-56; and S.G. Levin, S.L. Levin, and J.B. Meisel, “A Dynamic Analysis of the Adoption of New Technology: The Case of Optical Scanners,” Review of Economics and Statistics, Vol. 69 (1987), pp. 12-17. [3] E. Mansfield, Industrial Research and Technological Innovation (New York, NY: W.W. Norton, 1968). [4] P.A. David, A Contribution to the Theory of Diffusion, Stanford Center for Research in Economic Growth, Memorandum 71 (Stanford, CA: Stanford University, 1969). [5] R. Sutherland, “Market Barriers to Energy Efficiency Investments,” Energy Journal, Vol. 12, No. 3 (1991), pp. 15-34. [6] Learning-by-doing has been well documented since the 1930s for a wide variety of industries. For example, learning-by-doing has been documented for airframes, automobile assembly, chemical engineering, clerical activities, housing construction, machine tools, metal products, nuclear plant construction, petroleum refining, printing and typesetting, radar, rayon, and semiconductors. See T. Wright, “Factors Affecting the Cost of Airplanes,” Journal of Aeronautical Science, Vol. 3, No. 4 (1936), pp. 122-128; A. Alchian, “Reliability of Progress Curves in Airframe Production,” Econometrica, Vol. 31 (1963), pp. 679-693; N. Baloff, “Extension of the Learning Curve,” Operations Research Quarterly, Vol. 22 (1971), pp. 329-340; M. Lieberman, “The Learning Curve and Pricing in the Chemical Processing Industries,” RAND Journal of Economics, Vol. 15, No. 2 (1984), pp. 213-228; M. Kilbridge, “A Model for Industrial Learning,” Management Science, Vol. 8 (1962); J. Dejong, “The Effects of Increasing Skill on Cycle Time and Its Consequences for Time Standards,” Ergonomics, Vol. 1, No. 1 (1958), pp. 51-60; W.Z. Hirsch, “Process Functions of Machine Tool Manufacturing,” Econometrica, Vol. 20, No. 1 (1952), pp. 81-82; L. Dudley, “Learning and Productivity Changes in Metal Products,” American Economic Review, Vol. 62 (1972), pp. 662-669; R. Cantor and J. Hewlett, “The Economics of Nuclear Power: Further Evidence of Learning, Economies of Scale, and Regulatory Effects,” Resources and Energy, Vol. 10 (1988); W.B. Hirschmann, “Profits from the Learning Curve,” Harvard Business Review, Vol. 42, No. 1 (1964), pp. 125-139; F. Levy, “Adaptation in the Production Process,” Management Science}plain , Vol. 11 (1965), pp. B136-B154; L. Preston and E. Keachie, “Cost Functions and Progress Functions: An Integration.” American Economic Review, Vol. 54, No. 2 (1964), pp. 100-107; R.S. Jarmin, “Learning by Doing and Competition in the Early Rayon Industry,” RAND Journal of Economics, Vol. 25, No. 3 (1994), pp. 441-454; D. Webbinick, The Semiconductor Industry: A Survey of Structure, Conduct and Performance. Staff report to the Federal Trade Commission (Washington, DC: U.S. Government Printing Office, 1972); A.R. Dick, “Learning by Doing and Dumping in the Semi-conductor Industry,” Journal of Law Economics, Vol. 34 (1991), pp. 134-159; D.A. Irwin and P.J. Klenow, “Learning by Doing Spillovers in the Semiconductor Industry,” Journal of Political Economy, Vol. 102, No. 6 (1994), pp. 1200-1227; and N.W. Hatch and D.C. Mowery, “Process Innovation and Learning by Doing in Semiconductor Manufacturing,” Management Science, Vol. 44, No. 11 (November 1998). [7] T. Wright, “Factors Affecting the Cost of Airplanes,” Journal of Aeronautical Science, Vol. 3, No. 4 (1936), pp. 122-128. [8] N.W. Hatch and D.C. Mowery, “Process Innovation and Learning by Doing in Semiconductor Manufacturing,” Management Science, Vol. 44, No. 11 (November 1998). [9] A.R. Alvarez, “Process Requirements Through 2001,” presented at the Second International Rapid Thermal Processing Conference (1994). [10] N.W. Hatch and D.C. Mowery, “Process Innovation and Learning by Doing in Semiconductor Manufacturing,” Management Science, Vol. 44, No. 11 (November 1998). [11] See Energy Information Administration, The National Energy Modeling System: An Overview, DOE/EIA-0581(98) (Washington, DC, February 1998). The Appendix to this paper provides a brief overview. [12] A.D. Little, Inc., EIA—Technology Forecast Updates—Residential and Commercial Building Technologies—Reference Case, No. 37125-00 (Washington, DC, September 1998); A.D. Little, Inc., EIA—Technology Forecast Updates—Residential and Commercial Building Technologies— Advanced Adoption Case, No. 37125-00 (Washington, DC, September 1998). [13] E.W. Merrow, K.E. Phillips, and C.W. Myers, Understanding Cost Growth and Performance Shortfalls in Pioneer Process Plants (Santa Monica, CA: The RAND Corporation, 1981). [14] Adaptive expectations estimates future prices or quantities based on recent trends. These could be, for example, an extrapolation of the previous 5 years’ rates of change. [15] Rational expectations means that the module uses all the information available to the model about the past, present, and future. For dynamic optimization models, rational expectations means “perfect foresight.” For NEMS, this means expectations about the future based on a previous NEMS solution of a similar scenario. [16] R. S. Pindyck, “Investments of Uncertain Costs,” Journal of Financial Economics, Vol. 34 (1993), pp. 53-76. [17] Overnight costs, the capital costs of a plant if the plant were built and paid for overnight, are sometimes known as specific costs. For example, if the capital cost of a plant is $600 per kilowatt and 400 megawatts were built overnight, then the “overnight cost” would be $240 million. [18] D.F. Abell and J.S. Hammond, Strategic Planning: Problems and Analytical Approaches (Englewood Cliffs, NJ: Prentice-Hall, 1979). [19] Energy Information Administration, Modeling Technology Penetration, NEMS Component Design Report (Draft, April 7, 1993). [20] The two cases used are from Energy Information Administration, Annual Energy Outlook 1999, DOE/EIA-0383(99) (Washington, DC, December 1998), and Energy Information Administration, Impacts of the Kyoto Protocol on U.S. Energy Markets and Economic Activity, SR/OIAF/98-03 (Washington, DC, October 1998). In the Kyoto Protocol analysis, one of the cases analyzed (the 1990-7% case) required the United States to meet a carbon emissions target of 7 percent below 1990 levels entirely through domestic actions. [21] A biomass generator can be viewed as a biomass material handler and gasifier at the front end of a gas combined-cycle system. The “front end” of the biomass unit processes the biomass material (shreds it to an acceptable size and consistency) and then gasifies it. Material handling problems, which tend to gum up and seize the front-end processing, currently are expected to cause significant increases in scheduled and unscheduled maintenance. Both parts (the front-end and gas combined cycle) are expected to decline in costs although the material handling component and gasifier have the most room for learning. [22] n = 5 for the current implementation. [23] For example, see L. Neij, “Use of Experience Curves To Analyze the Prospects for Diffusion and Adoption of Renewable Energy Technology,” Energy Policy, Vol. 23 (1997), pp. 1099-1107; and L. Neij, Dynamics of Energy Systems: Methods of Analyzing Technology Change (Doctoral Dissertation) (Lund University, Sweden: Department of Environmental and Energy Systems Studies, 1999). [24] International Energy Agency, International Workshop on Experience Curves for Policy Making (Stuttgart, Germany, May 10-11, 1999). It was noted at the conference that the most recent advance in improving wind capacity factors was the use of larger wind blades. Participants also noted that further increases in wind blades could not be accommodated without an overall redesign of wind systems and at least some initial cost increases. [25] Experience with nuclear plants suggests that we cannot simply add up capacity every year, because time, operating experience, and engineering resources are required to achieve significant learning. [26] See Energy Information Administration, The National Energy Modeling System: An Overview, DOE/EIA-0581(98) (Washington, DC, February 1998). [27] The electricity and refinery sectors are the conversion modules. [28] Crude oil and product supply curves are produced using the World Oil Refining, Logistics, and Demand Model. Figure 1. Lower 48 Natural Gas Wellhead Prices in Three Cases, 1970-2020. Source: Energy Information Administration, Annual Energy Outlook 1999, DOE/EIA-0383(99) (Washington, DC, December 1998), Figure 98, p. 74. Figure 2. Example of Learning: Buildings Sector Technologies. Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Figure 3. Capital Costs for Compact Fluorescent Lighting in the AEO98 Reference Case. Source: Energy Information Administration, Assumptions to the Annual Energy Outlook 1998, DOE/EIA-0554(98) (Washington, DC, December 1997). Figure 4. Overview of the NEMS Electricity Market Module (EMM). Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Figure 5. Technological Learning. Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Figure 6. Electricity Technology Adjustment and Characterization. Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Figure 7. Lock-Out Example. Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Figure 8. Implied Subsidy Cost, Undiscounted. Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Figure 9. Learning in AEO99 and in the Kyoto Protocol 1990-7% Case. Sources: Energy Information Administration, Annual Energy Outlook 1999, DOE/EIA-0383(99) (Washington, DC, December 1998), and Energy Information Administration, Impacts of the Kyoto Protocol on U.S. Energy Markets and Economic Activity, SR/OIAF/98-03 (Washington, DC, October 1998). Figure A1. Structure of the National Energy Modeling System. Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. |
